CML

The Center for Machine Learning (CML) at the University of Maryland is located within the Institute for Advanced Computer Studies. The CML has a cluster of computational (CPU/GPU) resources that are available to be scheduled.

Compute Infrastructure

Each of UMIACS cluster computational infrastructures is accessed through the submission node. Users will need to submit jobs through the SLURM resource manager once they have logged into the submission node. Each cluster in UMIACS has different quality of service (QoS) that are required to be selected upon submission of a job and many like this one has specific other resources such as GPUs that need to be requested for a job.

The current submission node(s) for CML are:

• cmlsub00.umiacs.umd.edu

The Center for Machine Learning GPU resources are a small investment from the base Center funds and a number of investments by individual faculty members. The schedulers resources therefore going forward are modeled around this concept. This means there are additional Slurm accounts that users will need to be aware of if they are submitting in the non-scavenger partition.

Partitions

There are two partitions to the CML SLURM computational infrastructure. If you do not specify a partition when submitting your job you will receive the dpart.

• dpart - This is the default partition and job allocations are guaranteed.
• scavenger - This is the alternate partition that allows jobs longer run times and more resources but is preemptable when jobs in the dpart are ready to be scheduled.

Accounts

The Center has a base account cml which has a modest number of nodes (currently 16 GPUs) total that available in it. For other faculty who have invested they have an additional account provided which provides guaranteed GPU resources that they invested. If you do not specify a account when submitting your job you will receive the cml account.

$ sacctmgr show accounts
   Account                Descr                  Org
---------- -------------------- --------------------
       cml                  cml                  cml
   furongh         furong huang                  cml
      john       john dickerson                  cml
      root default root account                 root
 scavenger            scavenger            scavenger
    sfeizi         soheil feizi                  cml
      tomg        tom goldstein                  cml

You can check your account associations by running the show_assoc to see the accounts you are associated with. Please contact staff@umiacs.umd.edu and CC your faculty member if you do not see the appropriate association.

$ show_assoc
      User    Account   Def Acct   Def QOS                                  QOS
---------- ---------- ---------- --------- ------------------------------------
      tomg       tomg                                       default,high,medium
      tomg        cml                                            default,medium
      tomg  scavenger                                                 scavenger

You can also see the total number of Track-able Resources (TRES) allowed for each account by running the following command. Please make sure you give the appropriate account that you are looking for.

$ sacctmgr show assoc account=tomg format=user,account,qos,grptres
      User    Account                  QOS       GrpTRES
---------- ---------- -------------------- -------------
                 tomg                        gres/gpu=24
...

QoS

CML currently has 3 QoS for the dpart and 1 QoS for the scavenger partition. You are required to specify a QoS when submitting your job. The important parts here is that in different QoS you can have a shorter/longer maximum wall time, a total number of jobs running at once and a maximum number of track-able resources (TRES) for the job. In the scavenger QoS there is one more constraint that you are restricted by the total number of TRES per user (over multiple jobs).

# show_qos
      Name     MaxWall MaxJobs                        MaxTRES     MaxTRESPU
---------- ----------- ------- ------------------------------ -------------
    medium  3-00:00:00       1       cpu=8,mem=64G,gres/gpu=2
   default  7-00:00:00       2       cpu=4,mem=32G,gres/gpu=1
      high  1-12:00:00       1     cpu=16,mem=128G,gres/gpu=4
 scavenger  3-00:00:00                                          gres/gpu=36

GPUs

Jobs that require GPU resources need to explicitly request the resources within their job submission. This is done through Generic Resource Scheduling (GRES). Currently all nodes in the cluster are homogeneous however in the future this may not be the case. Users may use the most generic identifier in this case gpu a colon and a number to select without explicitly naming the type of GPU (ie. --gres gpu:4 for 4 GPUs).

$ sinfo -o "%15N %10c %10m  %25f %25G"
NODELIST        CPUS       MEMORY      AVAIL_FEATURES            GRES
cml[00-09]      32         1+          (null)                    gpu:rtx2080ti:8

Job Submission and Management

Users should review our SLURM job submission and job management documentation.

A very quick start to get an interactive shell is as follows when run on the submission node. This will allocate 1 GPU with 16GB of memory (system RAM) in the QoS default for 4 hours maximum time. If the job goes beyond these limits either the memory allocation or the maximum time it will be terminated immediately.

srun --pty --gres=gpu:1 --mem=16G --qos=default --time=04:00:00 bash

[derek@cmlsub00:~ ] $ srun --pty --gres=gpu:1 --mem=16G --qos=default --time=04:00:00 bash
[derek@cml00:~ ] $ nvidia-smi -L
GPU 0: GeForce RTX 2080 Ti (UUID: GPU-20846848-e66d-866c-ecbe-89f2623f3b9a)

If you are going to run in a faculty account instead of the default cml account you will need to specify the --account= flag.

Data Storage

Until the final storage investment arrives we have made available a temporary allocation of storage. This section is subject to change. There are 3 types of storage available to users in the CML home directories, project directories and scratch directories.

Home Directories

Home directories in the CML computational infrastructure are available from the Institutes NFShomes as /nfshomes/USERNAME where USERNAME is your username. These home directories have very limited storage and are intended for your personal files, configuration and source code. Your home directory is not intended for data sets or other large scale data holdings. Users are encouraged to utilize our GitLab infrastructure to host your code repositories.

NOTE: To check your quota on this directory you will need to use the quota -s command.

Your home directory data is fully protected and has both snapshots and is backed up nightly.

Project Directories

Users within the CML compute infrastructure can request project based allocations for up to 2TB for up to 120 days from staff@umiacs.umd.edu with approval from a CML faculty member and the director. These allocations will be available from /fs/cml-projects under a name that you provide when you request the allocation. Once the allocation period is over the user will be contacted and give a window of opportunity to clean and secure their data before staff will remove the allocation.

This data is backed up nightly.

Scratch Directories

There are two types of scratch directories in the CML compute infrastructure, network and local scratch directories. Scratch data has no data protection including no snapshots and the data is not backed up.

Network Scratch Directory

Users granted access to the CML compute infrastructure are each allocated 400GB of network attached scratch. This is available as /cmlscratch/USERNAME where USERNAME is your username. This directory is automounted so you will need to cd into the directory or request/specify a fully qualified file path to access this.

Users may request an additional allocation of scratch space up to 800GB by contacting staff@umiacs.umd.edu.

Local Scratch Directory

Each computational node that a user can schedule compute jobs on has one or more local scratch directories. These are always named /scratch0, /scratch1, etc. These are almost always more performant than any other storage available to the job. However users must stage their data within the confine of their job and stage the data out before the end of their job.

These local scratch directories will have a tmpwatch job which will delete unmodified data after 120 days. Please make sure you secure any data you write to these directories at the end of your job.

Data sets

The following data sets are available read only for the Center. If there are other data sets that you would like to see curated and available please contact staff@umiacs.umd.edu.

Data set	Path
CelebA	/fs/cml-datasets/CelebA
COCO	/fs/cml-datasets/coco
ImageNet ILSVRC2012	/fs/cml-datasets/ImageNet/ILSVRC2012
roberta	/fs/cml-datasets/roberta
ShapeNetCore.v2	/fs/cml-datasets/ShapeNetCore.v2
LSUN	/fs/cml-datasets/LSUN